In the two recent decades, although many methods of detecting concept drift have been proposed, they all, in general, follow a framework [1]} which includes four steps: 1) data retrieval aims to retrieve data chunks from data streams; 2) data modeling aims to abstract the retrieved data and extract the key features; 3) test statistics calculation is the measurement of dissimilarity, or distance estimation [2]}; 4) hypothesis testing uses a specific hypothesis test to evaluate the statistical significance of the change observed in step 3, or the p-value.
